
import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import plotly.express as px
import matplotlib


data = pd.read_csv("E:/insurance.csv")


data.head(5)


data.age.describe()

count    1338.000000
mean       39.207025
std        14.049960
min        18.000000
25%        27.000000
50%        39.000000
75%        51.000000
max        64.000000
Name: age, dtype: float64


import pandas as pd


data.smoker.value_counts()

no     1064
yes     274
Name: smoker, dtype: int64


px.histogram(data, x='smoker', color='sex', title='Smoker')


fig = px.histogram(data, 
                   x='age', 
                   marginal='box', 
                   nbins=47, 
                   title='Distribution of Age')
fig.update_layout(bargap=0.1)
fig.show()


fig = px.histogram(data, 
                   x='bmi', 
                   marginal='box', 
                   color_discrete_sequence=['blue'], 
                   title='Distribution of BMI (Body Mass Index)')
fig.update_layout(bargap=0.1)
fig.show()


fig = px.histogram(data, 
                   x ='charges', 
                   marginal='box', 
                   color='smoker', 
                   color_discrete_sequence=['green','blue'], 
                   title='Annual Medical Charges')
fig.update_layout(bargap=0.1)
fig.show()


fig = px.scatter(data, 
                 x='age', 
                 y='charges', 
                 color='smoker',  
                 hover_data=['sex'], 
                 title='Age vs. Charges')
fig.update_traces(marker_size=5) #to emphasize each point with a specifice maker
fig.show()


fig = px.scatter(data, 
                 x='bmi', 
                 y='charges', 
                 color='smoker',  
                 hover_data=['sex'], 
                 title='BMI vs. Charges')
fig.update_traces(marker_size=5)
fig.show()


data.charges.corr(data.age)

0.2990081933306474


data.charges.corr(data.bmi)

0.19834096883362903


data.children.corr(data.charges)

0.0679982268479047


data.corr()

C:\Users\Rajalaxmi Mohapatra\AppData\Local\Temp\ipykernel_10460\2627137660.py:1: FutureWarning:

The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.


sns.heatmap(data.corr(),cmap = 'Blues',annot = True)
plt.title = ("Co-reletation_matrix")

C:\Users\Rajalaxmi Mohapatra\AppData\Local\Temp\ipykernel_10460\3440050043.py:1: FutureWarning:

The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.


def estimate_charges(age, w, b):
    return w * age + b


##Let define a helper function estimate_charges, to compute  charges , given  age,  w and  b
##In the above case, the x axis shows "age" and the y axis shows "charges". Thus, we're assume the following relationship between the two:
##charges = w×age+b  

w = 50
b = 100


estimate_charges(30,w,b)

1600


non_smoker_df = data[data.smoker == 'no']


ages = non_smoker_df.age
estimated_charges = estimate_charges(ages, w, b)
estimated_charges

1       1000
2       1500
3       1750
4       1700
5       1650
        ... 
1332    2700
1333    2600
1334    1000
1335    1000
1336    1150
Name: age, Length: 1064, dtype: int64


non_smoker_df.charges

1        1725.55230
2        4449.46200
3       21984.47061
4        3866.85520
5        3756.62160
           ...     
1332    11411.68500
1333    10600.54830
1334     2205.98080
1335     1629.83350
1336     2007.94500
Name: charges, Length: 1064, dtype: float64


plt.scatter(ages, estimated_charges);
plt.plot(ages,estimated_charges)
plt.xlabel('Age');
plt.ylabel('Estimated Charges');


from sklearn.linear_model import LinearRegression
from sklearn.linear_model import SGDRegressor


model = LinearRegression()


inputs = non_smoker_df[['age']]
targets = non_smoker_df.charges
print('inputs.shape :', inputs.shape)
print('targes.shape :', targets.shape)

inputs.shape : (1064, 1)
targes.shape : (1064,)


model.fit(inputs, targets)

LinearRegression()

LinearRegression()


predictions = model.predict(inputs)


predictions

array([2719.0598744 , 5391.54900271, 6727.79356686, ..., 2719.0598744 ,
       2719.0598744 , 3520.80661289])


import math 
from sklearn.metrics import mean_squared_error


def rmse(targets, predictions):
    return np.sqrt(np.mean(np.square(targets - predictions)))


rmse(targets, predictions)

4662.505766636395


model.coef_

array([267.24891283])


model.intercept_

-2091.4205565650827


model1 = SGDRegressor()


model1.fit(inputs,targets)

SGDRegressor()

SGDRegressor()


predictions = model1.predict(inputs)
rmse(targets, predictions)

5075.193593045272


#Model creation for smoker dataset
smoker_df = data[data.smoker == 'yes']
smoker_df


inputs = smoker_df[['age']]
targets = smoker_df.charges
print('',inputs.shape)
print('',targets.shape)

 (274, 1)
 (274,)


model.fit(inputs,targets)

LinearRegression()

LinearRegression()


predicted = model.predict(inputs)
print('RMSE:',)
rmse(targets,predicted)

RMSE:

10711.00334810241


sns.barplot(data = data, x='smoker', y='charges');


smoker_codes = {'no': 0, 'yes': 1}
data['smoker_code'] = data.smoker.map(smoker_codes)


data.charges.corr(data.smoker_code)

0.7872514304984761


data


# Create inputs and targets
inputs, targets = data[['age', 'bmi', 'children', 'smoker_code']], data['charges']

# Create and train the model
model = LinearRegression().fit(inputs, targets)

# Generate predictions
predictions = model.predict(inputs)

# Compute loss to evalute the model
loss = rmse(targets, predictions)
print('Loss:', loss)

Loss: 6056.439217188081


sns.barplot(data=data, x='sex', y='charges')

<Axes: xlabel='sex', ylabel='charges'>


sex_codes = {'female': 0, 'male': 1}


data['sex_code'] = data.sex.map(sex_codes)


data


data.charges.corr(data.sex_code)

0.057292062202025464


# Create inputs and targets
inputs, targets = data[['age', 'bmi', 'children', 'smoker_code', 'sex_code']], data['charges']

# Create and train the model
model = LinearRegression().fit(inputs, targets)

# Generate predictions
predictions = model.predict(inputs)

# Compute loss to evalute the model
loss = rmse(targets, predictions)
print('Loss:', loss)

Loss: 6056.100708754546


sns.barplot(data = data, x='region', y='charges');


#Model Improvements
#Feature Scaling
#Recall that due to regulatory requirements, we also need to explain the rationale behind the predictions our model.

#charges=w1×age+w2×bmi+w3×children+w4×smoker+w5×sex+w6×region+b
 
#To compare the importance of each feature in the model, our first instinct might be to compare their weights.


model.coef_

array([  257.73498767,   322.36421449,   474.41112061, 23823.39253065,
        -128.63985357])


model.intercept_

-12052.461985664726


data


from sklearn.preprocessing import StandardScaler


numeric_cols = ['age', 'bmi', 'children'] 
scaler = StandardScaler()
scaler.fit(data[numeric_cols])

StandardScaler()

StandardScaler()


scaler.mean_

array([39.20702541, 30.66339686,  1.09491779])


scaler.var_

array([197.25385199,  37.16008997,   1.45212664])


data[numeric_cols]


scaled_inputs = scaler.transform(data[numeric_cols])
scaled_inputs

array([[-1.43876426, -0.45332   , -0.90861367],
       [-1.50996545,  0.5096211 , -0.07876719],
       [-0.79795355,  0.38330685,  1.58092576],
       ...,
       [-1.50996545,  1.0148781 , -0.90861367],
       [-1.29636188, -0.79781341, -0.90861367],
       [ 1.55168573, -0.26138796, -0.90861367]])


from sklearn.model_selection import train_test_split


inputs_train, inputs_test, targets_train, targets_test = train_test_split(inputs, targets, test_size=0.2,random_state=0)


# Create and train the model
model = LinearRegression().fit(inputs_train, targets_train)

# Generate predictions
predictions_test = model.predict(inputs_test)

# Compute loss to evalute the model
loss = rmse(targets_test, predictions_test)
print('Test Loss:', loss)

Test Loss: 5671.492452926755


# Generate predictions
predictions_train = model.predict(inputs_train)

# Compute loss to evalute the model
loss = rmse(targets_train, predictions_train)
print('Training Loss:', loss)

Training Loss: 6150.508349257895

	age	sex	bmi	children	smoker	region	charges
0	19	female	27.900	0	yes	southwest	16884.92400
1	18	male	33.770	1	no	southeast	1725.55230
2	28	male	33.000	3	no	southeast	4449.46200
3	33	male	22.705	0	no	northwest	21984.47061
4	32	male	28.880	0	no	northwest	3866.85520

	age	bmi	children	charges
age	1.000000	0.109272	0.042469	0.299008
bmi	0.109272	1.000000	0.012759	0.198341
children	0.042469	0.012759	1.000000	0.067998
charges	0.299008	0.198341	0.067998	1.000000